Del 14293 i serien Lecture Notes in Computer Science

Domain Adaptation and Representation Transfer

5th MICCAI Workshop, DART 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 12, 2023, Proceedings

Häftad, Engelska, 2023

AvLisa Koch,M. Jorge Cardoso,Enzo Ferrante,Konstantinos Kamnitsas,Mobarakol Islam,Meirui Jiang,Nicola Rieke,Sotirios A. Tsaftaris,Dong Yang

799 kr

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This book constitutes the refereed proceedings of the 5th MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2023, which was held in conjunction with MICCAI 2023, in October 2023. The 16 full papers presented in this book were carefully reviewed and selected from 32 submissions. They discuss methodological advancements and ideas that can improve the applicability of machine learning (ML)/deep learning (DL) approaches to clinical setting by making them robust and consistent across different domains.

Produktinformation

Utgivningsdatum2023-10-14
Mått155 x 235 x 11 mm
Vikt283 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor170
FörlagSpringer International Publishing AG
ISBN9783031458569

Tillhör följande kategorier

Tillämpad datateknik inom Data och IT
Informationsteknik: allmänt inom Data och IT
Systemvetenskap och AI inom Data och IT

Domain adaptation of MRI scanners as an alternative to MRI harmonization.- MultiVT: Multiple-Task Framework for Dentistry.- Black-Box Unsupervised Domain Adaptation for Medical Image Segmentation.- PLST: A Pseudo-Labels with a Smooth Transition Strategy for Medical Site Adaptation.- Compositional Representation Learning for Brain Tumor Segmentation.- Hierarchical Compositionality in Hyperbolic Space for Robust Medical Image Segmentation.- Realistic Data Enrichment for Robust Image Segmentation in Kidney Transplant Pathology.- Boosting Knowledge Distillation via Random Fourier Features for Prostate Cancer Grading in Histopathology Images.- Semi-supervised Domain Adaptation for Automatic Quality Control of FLAIR MRIs in a Clinical Data Warehouse.- Towards Foundation Models Learned from Anatomy in Medical Imaging via Self-Supervision.- The Performance of Transferability Metrics does not Translate to Medical Tasks.- DGM-DR: Domain Generalization with Mutual Information Regularized Diabetic Retinopathy Classification.- SEDA: Self-Ensembling ViT with Defensive Distillation and Adversarial Training for robust Chest X-rays Classification.- A Continual Learning Approach for Cross-Domain White Blood Cell Classification.- Metadata Improves Segmentation Through Multitasking Elicitation.- Self-Prompting Large Vision Models for Few-Shot Medical Image Segmentation.

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Produktinformation

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